Autoscaler architecture of apache stratos 4.0.0
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The document outlines the architecture and policies of the Apache Stratos autoscaler, focusing on components, event flow, and scaling strategies. It discusses various policies for automation in capacity planning, along with rules for scaling events, including minimum, scale up, and scale down rules. Additionally, the document addresses fault handling scenarios and provides links for further reference and community engagement.
Autoscaler architecture of apache stratos 4.0.0
- 1. Autoscaler Architecture Lahiru Sandaruwan Apache Committer - Stratos(Incubating) , Software Engineer - WSO2 Inc
- 2. Agenda • • Introduction to Autoscaling Apache Stratos Autoscaler Architecture • • • • Component Architecture • Event Flow Autoscale Policy • Introduction to Autoscaler Policy • Autoscaling Strategies Deployment Policy • Introduction to Deployment Policy • Capacity Planning with Deployment Policy • Partition Selection Algorithms Rules Engine • Reasons for a Rule Engine • Rules for Apache Stratos Autoscaler 1
- 3. Introduction to Autoscaling • • • • What is scalability • Horizontal and vertical scaling What is high availability Proceedure • Clustering • Load balancing Autoscaling • Automating the capacity planning 2
- 4. Introduction to Autoscaling Contd. • • • • • Flexible cloud solution • • User-defined policies, health status checks, and schedules. Use case, cost, performance, and infrastructure. SLA(Service Level Agreement) aware elastic cloud • • • QoS , SLA aware services Daecision factors to consumers Solves performance, availability, and economic costs issues Capacity planning • Automated control of cloud: cost vs. Qos, find appropriate cloud model. Cost Factor • Reduce economic cost and energy footprint Proceedure • • • • Online observation and monitoring the cloud Trigger an event if a SLA violation happened Use control theory and mathematical operations Handling seasonal patterns. E.g. Year ends/ Weekends patterns 3
- 6. Event Flow 5
- 7. Health Statistics as Events • CEP receives events • Requests in flight from Load balancer • Cartridge instance health statistics from Cartridge agent • CPU consumption • Memory consumption • CEP summarize the Average, Gradient, and Second derivative events of, • Requests in Flight • CPU consumption • Memory consumption 6
- 8. Autoscale Policy • Deployable Xml model • Keeps Load thresholds for threshold based rules evaluation. • Deployed by Dev-ops or similar role at start or later • Hot Deployable. • Users Selects an Autoscale Policy on His Preference at Subscription Time. 7
- 9. Autoscale Policy contd. <autoscalePolicy id="economy-autoscale"> <loadThresholds> <requestsInFlight> <average value="40" /> <gradient value="1" /> <secondDerivative value="0" /> <scaleDownMarginOfGradient value="1.0" /> <scaleDownMarginOfSecondDerivative value="0.2" /> </requestsInFlight> <memoryConsumption> ... </memoryConsumption> <loadAverage> ... </loadAverage> </loadThresholds> </autoscalePolicy> 8
- 10. Deployment Policy • Deployable xml model • Keeps the Capacity Planning. • Deployed by Dev-ops or similar role at start or later • Hot Deployable. • Users Selects an Deployment Policy on His Preference at Subscription Time. 9
- 11. Deployment Policy contd. <deploymentPolicy id =”economy-deployment”> <partitionGroups> <partitionGroup id=”group1” > <partitionAlgo>OneAfterAnother</partitionAlgo> <partition id="partition1" > <max>3</max> <min>1</min> </partition> <partition id="partition2" > <max>5</max> <min>1</min> </partition> <partition id="partition3"> <max>20</max> <min>1</min> </partition> </partitionGroup> ... </deploymentPolicy> 10
- 12. Rules Engine • • • Why a Rules Engine • • • Ease of use: No byte code and easy to modify Readable Performances and sclability Uses Drools engine as the default rules engine Rules • Minimum Rule • Scale Up Rule • Scale Down Rule • Terminate All Rule 11
- 13. Autoscaling Rules: Sample in Drools rule "Minimum Rule" dialect "mvel" when $service : Service () $cluster : Cluster () from $service.getClusters() $policy : AutoscalePolicy(id == $cluster.autoscalePolicyName ) from $manager.getPolicyList() $partition : Partition () from $policy.getHAPolicy().getPartitions() $clusterContext : ClusterContext() from $context.getClusterContext($cluster.getClusterId()) eval($clusterContext.getPartitionCount($partition.getId()) < $partition.getPartitionMembersMin() ) then int memberCountToBeIncreased = 1; if($evaluator.delegateSpawn($partition,$cluster.getClusterId(), memberCountToBeIncreased)){ $clusterContext.increaseMemberCountInPartition($partition.getId(), memberCountToBeIncreased); } end 12
- 14. Minimum Rule • This runs when a “cluster created” event is received • Scan through all the partitions of the cluster and find minimums • Call CC for spawning required minimum instances • This will be also run periodically(with a higher time interval than scale up/down rules) to assure that the minimum count is preserved 13
- 15. Scale Up/Down Rule • These rules run periodically • Evaluate load details(Received from CEP) against their thresholds(defined in Autoscale Policy). • Decide whether to scale up, scale down, or do nothing • Call CC for spawning instances in selected partitions 14
- 16. Autoscaling Strategies • • Threshold based autoscaling Predictive or proactive auto-scaling techniques • Kalman Filter • Control Theory • Time series analysis 15
- 17. Average of Requests In Flight at LB for a Specific Cluster 16
- 18. Average of CPU/ Memory Consumption for a Specific Cluster 17
- 19. Terminate All Rule • • • This runs when a “cluster removed” event is received Scan through all the partitions of the cluster and find member IDs to be terminated Call CC for terminating those instances 18
- 20. Fault Handling Scenarios Process VM Down Up Down Down(It can be that agent is crashed) Up Up(but network issue) Decision flow • • • • • • • • • Cartridge agent publish event to CC CC updates instance status in topology Autoscaler decides to kill it CEP identify that & publish event to Autoscaler Autoscaler calls CC to terminate(if available) and remove the instance from topology Autoscaler will spawn another to cover that CEP sends statistics on fault requests to Autoscaler Autoscaler keep monitoring it and takes a decision to terminate the instance Autoscaler will spawn another in the same partition to cover that 19
- 22. Join Us Website http://stratos.incubator.apache.org Mailing List Subscribe: dev-subscribe@stratos.incubator.apache.org Post (After subscription): dev@stratos.incubator.apache.org Social Media Google+: https://plus.google.com/103515557134069849802 Twitter: https://twitter.com/ApacheStratos Facebook: https://www.facebook.com/apache.stratos LinkedIn: http://www.linkedin.com/groups?home=&gid=5131436 21
- 23. Thank you 22